Patient interfaces, such as masks in pressure support systems, are used for delivering gas to a user (herein also referred to as “person” or “patient”). Such gases like air, cleaned air, oxygen, or any modification thereof are submitted to the user via the patient interface in a pressurized or unpressurized way.
For several chronic disorders and diseases, the usage of such a patient interface is necessary or at least advisable.
One non-limiting example of such a disease is obstructive sleep apnea or obstructive sleep apnea syndrome (OSA). OSA is usually caused by an obstruction of the upper airway. It is characterized by repetitive pauses in breathing during sleep and is usually associated with a reduction in blood oxygen saturation. These pauses in breathing, called apneas, typically last 20 to 40 seconds. The obstruction of the upper airway is usually caused by reduced muscle tonus of the body that occurs during sleep. The human airway is composed of walls of soft tissue which can collapse and thereby obstruct breathing during sleep. Tongue tissue moves towards the back of the throat during sleep and thereby blocks the air passages. OSA is therefore commonly accompanied with snoring. Different invasive and non-invasive treatments for OSA are known. One of the most powerful non-invasive treatments in the usage of Continuous Positive Airway Pressure (CPAP) or Bi-Positive Airway Pressure (BiPAP) in which a patient interface, e.g. a face mask, is attached to a hose and a machine that blows pressurized gas, preferably air, into the patient interface and through the airway of the patient in order to keep it open. Positive air pressure is thus provided to a patient through a hose connected to a patient interface or respiratory interface, such as a face mask, that is worn by the patient. The afore-mentioned long-term use of the patient interface is the result, since the wearing of the patient interface usually takes place during the sleeping time of the patient.
Examples for patient interfaces are:
nasal masks, which fit over the nose and deliver gas through the nasal passages,
oral masks, which fit over the mouth and deliver gas through the mouth,
full-face masks, which fit over both, the nose and the mouth, and deliver gas to both,
total-face masks, which cover the full face or substantially the full face, surrounding the nose, mouth as well as the eyes and delivering gas to the mouth and nose, and
nasal pillows (also referred to as alternative masks), which are regarded as masks as well within the scope of the present invention and which consist of small nasal inserts that deliver the gas directly to the nasal passages.
In order to guarantee a reliable operation of the device, the patient interface (mask) needs to closely fit on the patient's face to provide an air-tight seal at the mask-to-face interface. Usually, the patient interface is worn using a head gear with straps that go around the back of the patient's head. The patient interface or mask in practice usually comprises a soft cushion that is used as mask-to-patient interface, i.e. that contacts the face of the patient when the mask is worn, as well as it usually comprises a so-called mask shell building a rigid or semi-rigid holding structure for holding the cushion in place and for supplying mechanical stability to the patient interface (mask).
The cushion usually comprises one or more pads made of gel or silicone or any other soft material in order to increase the patient comfort and guarantee a soft feeling on the patient's face. The latter-mentioned mask shell usually also comprises a hose interface that is adapted for connecting the air supplying hose to the mask. Depending on the type of the mask, it may also comprise a mechanism with an additional cushion support on the forehead to balance the forces put by the mask around the airway entry features of the human face.
It is evident that a close and correct fit of the patient interface is of utmost importance for a reliable operation of the device. An incorrect fit of the patient interface may not only lead to unwanted air leaks at the mask-to-face interface, but may also cause excessive pressure points on the skin of the patient's face that again may cause unpleasant and painful red marks in the patient's face. The patient interface, therefore, needs to be accurately fitted to the individual face contours of the patient. Various types of patient interfaces exist, i.e. not only different sizes and shapes, but also different types of patient interfaces. As the anatomical features of faces differ from patient to patient, the best fitting patient interface also differs from patient to patient. In other words, an individualized fitting or selection of a patient interface is required, and it is evident that a good fitting or selection of a patient interface relies on a correct measurement or estimation of the absolute facial dimensions of the patient.
A mask fitting system that makes use of a simplified fitting technique is known from US 2006/0235877 A1. The mask fitting system and method described therein determine the dimensions of the patient's head with a template or a ruler. Alternatively, one or more images of the patients are captured and then the dimensions of the patient's head are manually typed into the system using a questionnaire that has to be filled out by the patient. In any case, the absolute facial dimensions need to be either manually measured or inputted into the system by the patient filling out the questionnaire. This is, of course, bothersome and time-consuming for the user. Apart from that, a manual measurement of the facial dimensions requires an educated person to conduct the measurements, and it is error prone to subjective interpretation of physiological facial landmarks.
In many practical appliances the facial dimensions cannot be measured manually (since there is no time) or no absolute dimensions of the user's face are known in advance, so that the device and method proposed in US 2006/0235877 A1 is not only disadvantageous, but can also not be applied in many practical situations.
Alternatively, it is also possible to use a calibrated optical scanner in order to receive the absolute facial dimensions. However, the use of such calibrated optical scanners cannot be perceived as a commodity yet. Apart from that, such calibrated optical scanners are quite expensive in production and, at least so far, they do not seem to be suitable as everyday devices in a private or semi-professional surrounding.
It would therefore be neat if one could reconstruct or estimate the facial dimensions from a “regular” photo or video. Modern computer vision technologies allow accurate reconstruction of the facial shape using a single (mono) video camera (see e.g. Jeni, L. A. et al.: Dense 3D Face Alignment from 2D Videos in Real-Time, the Robotics Institute, Carnegie Mellon University, Pittsburgh, Pa., USA, zface.org). By using such a technique the input to an application for advising, selecting and/or fitting a patient interface could be then a single photograph (selfie) or a short video taken with a regular smartphone (mono) camera. However, it is important to note that the afore-mentioned method only allows an accurate reconstruction of the shape of the face of a person but not a reconstruction of the absolute dimensions of the face of the person. In other words, a direct measurement of the reconstructed facial model is not possible due to the scale ambiguity, as the absolute dimensions of the reconstructed object cannot be recovered using a single (mono) camera setup. The true scale of the facial model may thus not be determined in an automated way when using the afore-mentioned method.
Thus, there is still room for improvement.
One of the most popular local texture models for face alignment is a model known as Active Shape Model (ASM). This model applies in many fields, including the field of locating facial features in an image, and the field of face synthesis. Le Hoang Thai; Vo Nhat Truong et al: “Face Alignment Using Active Shape Model And Support Vector Machine”, International Journal of Biometric and Bioinformatics, 1 Feb. 2011, pages 224-234, XP055277739, relates to improving the ASM so as to have increased performance of the ASM for face alignment applications of the model. The improvements include using a Support Vector Machine (SVM) to classify landmarks on a detected face in an image, and automatically adjusting a 2-D profile in a multi-level model based on the size of the input image. In the process, an alignment (scaling, rotation and translation) of a model on the face in the image is performed.
Xiong Xuehan et al: “Supervised Descent Method and Its Applications to Face Alignment”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Proceedings, IEEE Computer Society, US, 23 Jun. 2013, pages 532-539, XP032492802, relates to a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent direction without computing the Jacobian nor the Hessian. In particular, it is shown how SDM achieves state-of-the-art performance in the field of facial feature detection.